Sportsmanship, a key dimension of organizational citizenship behavior (OCB), refers to employees' willingness to maintain a positive attitude at work by tolerating inconveniences and avoiding unnecessary complaints. Although sportsmanship behavior plays an important role in sustaining cooperative and supportive workplace environments, relatively little research has examined the trust-related mechanisms that encourage it. Drawing on Social Exchange Theory (SET) and Leader-Member Exchange (LMX), this study examines how trust-based relationships within organizations influence employees' sportsmanship behavior. Specifically, the study proposes that trust in the manager contributes to sportsmanship behavior both directly and indirectly through organizational trust. When employees perceive their managers as competent, fair, and supportive, they are more likely to interpret their interactions with managers as positive exchanges. According to SET, employees tend to reciprocate these positive experiences through constructive discretionary behaviors that benefit the organization. Over time, trust in the manager may extend beyond the interpersonal level and develop into a broader sense of trust in the organization. Data from 287 employees in Saudi Arabia were analyzed using PLS-SEM to examine how trust influences workplace behavior. The results indicate that when employees trust their manager, they are more likely to demonstrate sportsmanship behavior. Trust in the manager also strongly strengthens trust in the organization. In turn, organizational trust encourages sportsmanship behavior and partially explains how managerial trust translates into more positive behavior at work. Drawing on SET and LMX, the findings emphasize that building trustful manager-employee relationships can strengthen organizational trust and support a more cooperative, supportive work environment.
ObjectivesThe objective of the study is to examine associations between connections to the workforce and trust in the health care system in the United States.MethodsWe examine cross-sectional survey data from the 2024 Health Information National Trends Survey (HINTS, n = 7,278) descriptively and statistically. After examining sample characteristics, trust is examined by income and primary employment status. Bivariate and multivariate ordinary least squares regression models are created to examine associations of trust in the health care system.ResultsThere are associations between connections to the workforce and trust in the United States' health care system. Students, retired people, and homemaker/caregivers have comparatively high trust in the health care system, although the association for retired people loses significance with controls and homemaker/caregiver findings are only significant with controls. People with a higher household income tend to trust the health care system more than people with a lower household income.ConclusionAccess to and payment for health care in the United States is interwoven with connections to the workforce, with trust seemingly associated with an ability to pay as indicated by income and employment status. Trust in the health care system is still high but is lower among key groups, especially those with lower household incomes and key occupational groups. Trust in this study was highest among those with lower workforce obligations who may still have indirect access to health care payment such as homemaker/caregivers, students, and older adults with access to public health care programs. These findings highlight the need to prioritize outreach and communication with different kinds of people who have low trust in the health care system.
Reliable fault diagnosis of milling machines under varying operating conditions remains challenging due to distribution shifts caused by speed variations, nonstationary dynamics, and limited labeled data in target domains. Conventional domain adaptation methods often assume equal reliability across samples and neglect the varying physical consistency of signals collected under different conditions. To address this limitation, this study proposes trust-aware domain adaptation network for cross-domain fault diagnosis that integrates physics-guided reliability estimation with deep representation learning. In the proposed framework, physically interpretable global and local features are first extracted from multi-channel vibration signals using energy, spectral, nonlinear, and impulsiveness descriptors. A dedicated Physics Trust Network is then introduced to estimate per-sample trust scores that quantify the physical reliability of each signal based on its physics feature consistency. These trust scores are explicitly embedded into representation learning through a trust-weighted feature encoder, ensuring that physically reliable samples contribute more strongly to the learned latent space. To address distribution mismatch between source and target conditions, a trust-weighted covariance alignment strategy is introduced, enabling domain adaptation to be guided by reliable samples instead of treating all data equally. In this way, the model simultaneously learns discriminative, transferable, and physically consistent features. The entire framework is trained end-to-end using labeled source data and unlabeled target data, enabling effective knowledge transfer under cross-speed conditions. Extensive experiments on a real milling machine dataset collected at different spindle speeds demonstrate that the proposed framework achieves an average accuracy of 98.07%, performing better than two recent state-of-the-art domain adaptation approaches by a significant margin. Ablation experiments further confirm that reliability estimation, trust-weighted representation learning, and trust-guided alignment each contribute independently to performance improvement.
This paper proposes a trust-aware environmental state consensus framework for smart agriculture that integrates TEE-enabled sensing, Byzantine-resilient aggregation, and lightweight blockchain-based state coordination under resource-constrained IoT environments. Unlike conventional IoT systems that treat blockchain as a transactional ledger for directly storing sensor outputs, the proposed framework utilizes blockchain as a state commitment layer that records only validated environmental state transitions. In the proposed architecture, distributed sensor readings are modeled as noisy and potentially adversarial observations of an underlying physical state rather than directly trusted measurements. To establish a reliable trust boundary between physical sensing and distributed coordination, TEE-enabled sensing devices provide authenticated and integrity-protected data outputs before blockchain processing. The TEE component is adopted as a deployed trusted execution anchor rather than a newly designed hardware security mechanism, and its role is to protect sensing-side execution and provide trustworthy inputs for subsequent coordination. Since trusted execution alone cannot guarantee the correctness of sensor observations, a Byzantine-resilient aggregation mechanism is introduced to estimate consistent environmental states under faulty or adversarial sensing conditions. The validated states are then committed through a lightweight permissioned blockchain to provide tamper-evident state finality using a K-confirmation-based commitment mechanism. The proposed framework is implemented and evaluated on a real greenhouse IoT platform with distributed sensing nodes and edge computing devices. Experimental results demonstrate that the proposed approach improves environmental state consistency under varying adversarial conditions while maintaining stable blockchain coordination and resource-aware execution performance.
Integration of artificial intelligence chatbots into healthcare requires rigorous, patient-centered evaluation. This study implements the CREATE TRUST framework-a novel tool evaluating both clinical substance and communication style-to compare responses from healthcare providers (HCPs) and two AI chatbots (GPT-4, Mixtral) to complex clinical questions. In this cross-sectional study, 189 real-world clinical messages from patients with cancer were retrospectively collected from an electronic health record. Anonymized and randomly ordered responses from the HCP, GPT-4, and Mixtral were blindly evaluated in triplicate by a team of oncologists. Evaluators rated each response on every attribute of CREATE TRUST (Correct, Referenced, Empathic, Authentic, Thorough, Engaging, Tailored, Respectful, Understandable, Safe) and provided an overall preference ranking. While HCP responses were ranked first most often (46% of evaluations), there was no significant difference in the overall CREATE TRUST score between HCPs (mean=28.9), GPT-4 (28.4), and Mixtral (28.1). HCPs performed significantly better on Authentic and Tailored. Chatbots scored higher on Empathic and Referenced. Performance was comparable for all other attributes. HCPs demonstrated greater performance variability, authoring a higher proportion of both high- and low-quality responses. HCPs and AI chatbots exhibit comparable overall quality but possess distinct, complementary strengths. HCPs excel in authentic, tailored communication, while chatbots provide more empathic and referenced responses. These findings suggest potential for a synergistic workflow where AI could enhance human-authored messages, improving targeted aspects of communication and mitigating low-quality responses.
Byzantine-resilient multi-agent reinforcement learning (MARL) matters in networked cyber-physical systems, where corrupted sensor messages degrade formation accuracy and execution-time safety. This paper presents an evaluation and audit study: a multiplicity-corrected operating-regime protocol applied to RS-MARL, a representative trust-based safety pipeline. The aim is to identify supported, inconclusive, and detector-limited regimes rather than claim a universally superior new MARL algorithm. The evidence base contains a 3000-run core matrix over five methods, six attack families, five Byzantine ratios, and 20 seeds per cell; 580 benign-control and ablation runs; and a 2380-run review-audit extension covering A-CBF calibration, four-switch ablation, sensor impairment, and high-seed confirmation. Results are regime-specific. RS-MARL has lower mean safety violations than Safe-MAPPO in 19 of 30 attack-ratio cells, but no core contrast survives Holm correction. Detection is reliable under collusive, random, and stealthy attacks, but weak or undefined under constant, adaptive, and sign-flip attacks, which bound the current energy-based trust detector's operating envelope. A-CBF margin retuning does not improve over the deployed setting after correction, while four-switch ablation identifies SET as independently necessary for collusive-attack detection. The results support a reproducible reporting template: matched baselines, sensitivity estimates, detection reliability, artefact audits, and explicit safety-performance trade-offs.
Health literacy (HL) is increasingly recognized not merely as an individual asset, but as a structural determinant of health equity and a critical governance instrument for sustainable health systems. While frameworks for "health literate organizations" are well-established in high-income contexts, low- and middle-income countries (LMICs) face similar systemic barriers-including centralized governance, fragile social trust, and resource constraints-that require distinct implementation roadmaps.In this perspective we argue that for health systems in the Middle East and North Africa (MENA) region to succeed, they must pivot from a didactic, education-centric paradigm toward an empowerment-oriented governance model. Accordingly, we articulate a multidimensional framework structured around five interdependent pillars: 1. structural reform and high-level advocacy, 2. mobilizing social capital and trust, 3. human resource empowerment, 4. primary health care transformation, and 5. sustainable financing. Drawing on global evidence-including WHO frameworks, the Ottawa Charter, and Health in All Policies-this paper proposes a multidimensional roadmap that institutionalizes HL as a core accountability mechanism within reginal health governance in the MENA region. This model offering a politically astute and transferable framework for LMICs seeking to embed health equity within the architecture of the state.
Primary health care (PHC) systems across many low- and middle-income countries increasingly rely on mixed provider environments involving public, private, and mission-based actors. Public-private partnerships (PPPs) are often promoted as a way of improving coordination and expanding access to care. However, many PHC partnerships continue to be governed through highly centralized and compliance-oriented arrangements that may limit flexibility, trust, and long-term collaboration. Botswana provides a useful case for examining how PPPs are governed within a health system experiencing fiscal pressure, medicines supply challenges and ongoing PHC restructuring. This study relies on a theory-based qualitative documentary analysis of policy documents, government reports, gray literature, and peer-reviewed research relevant to PHC governance and PPP implementation in Botswana and comparable low- and middle-income country settings. Concepts such as health systems governance, stewardship and systems thinking guided the analysis with due attention to financing arrangements, accountability systems, provider relationships and implementation dynamics within mixed health systems. Four recurring governance barriers were identified as factors affecting PPP implementation in PHC. These included hierarchical administrative approaches that limit collaboration and local adaptation; weak financing credibility and purchasing uncertainty; limited community-facing accountability; and gaps between national policy ambition and district-level implementation capacity. The analysis also identified emerging opportunities related to digital health systems, integrated service delivery approaches, and more flexible partnership practices that may strengthen coordination across providers. The findings suggest that PPP performance in PHC depends less on formal contracts alone and more on the wider governance conditions shaping relationships between public institutions, providers, and communities. In Botswana, partnership arrangements are more likely to function effectively when financing systems are credible, accountability extends beyond administrative reporting, and local implementation systems are adequately supported. The study contributes to wider debates on PHC governance by highlighting the importance of stewardship, coordination, and institutional trust in sustaining partnerships within mixed health systems.
The decision by the co-Editor-in-Chief of Regulatory Toxicology and Pharmacology, Prof. Martin van den Berg, to retract the 2000 review article by Williams, Kroes, and Munro has elicited widespread criticism within the scientific community. Issued in late 2025, the retraction decision cites procedural concerns including potential ghostwriting, undisclosed conflicts of interest, and omission of certain unpublished studies, invoking Committee on Publication Ethics guidelines despite lacking evidence of fraud or scientific flaws. This editorial argues that the retraction decision involves editorial overreach and misapplication of the guidelines. The alleged omissions stemmed from proprietary data access limitations that were disclosed in the original paper. Subsequent reviews by several independent expert panels and regulatory authorities with access to all glyphosate data, including the studies cited by the retracting editor, reached similar conclusions. Claims of ghostwriting were previously investigated and found lacking, including a declaration by EFSA as to the clarity of the conflict disclosures. The retraction's timing, reliance on litigation documents, and apparent biases that were not disclosed in the retraction notice raise questions of ideological interference. Absent substantive rebuttals based on scientific merit rather than speculative claims of inappropriate authorship and data access, this retraction decision sets a dangerous precedent for retroactive censorship, potentially chilling beneficial industry-academic collaborations and eroding trust in the integrity of scientific publishing. With the strongest conviction, we assert that retracting a paper without scientific flaws isn't protection-it is censorship. We therefore call for the immediate reversal of this flawed and unjustified retraction to preserve trust in peer-reviewed literature.
Internet of Things (IoT) environments, which are distributed and resource-constrained, present unique security challenges, making it essential to develop robust and transparent intrusion detection system (IDS) solutions. This study presents a specialized system for IoT environments that combines hybrid feature selection methods with anomaly detection algorithms and classification strategies, alongside explainability techniques, to enhance security measures and event transparency. The novelty of this work lies in combining several modern approaches: hybrid feature selection by combining Random Forest (RF) and SelectKBest to reduce computational overhead while preserving high accuracy of detection; the application of Deep Autoencoders (DAEs) for detecting anomalous traffic deviating from learned normal behavior, enabling detection of previously unseen attack patterns under controlled experimental conditions; feedforward neural networks (FNNs) are applied to classify anomalous data with high accuracy and reduced training time, and explainability tools such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) are incorporated to provide insights into model decisions and improve trust. We utilize the CIC-IDS2017 and EIIoT datasets to evaluate their effectiveness in identifying critical cyber threats and subsequently classifying them. This proposed framework, HDATL-XAI (Hybrid Dimension-reduction Autoencoder and Traditional Learning with Explainable Artificial Intelligence), integrates advanced techniques to offer comprehensive protection while ensuring transparency, enhancing security and trustworthiness, and serving as an essential tool for protecting IoT networks.
Low-cost multi-pollutant sensors make personal exposure monitoring affordable, but assuring their data quality in the field is the bottleneck, while current devices leave it to remote servers: the field unit is a passive terminal that cannot self-check its sensors, takes days to accept a new one, and loses quality control whenever connectivity drops. We develop Zhiwei, an on-device edge AI agent for personal exposure monitoring that brings the reasoning loop onto the device, so it can diagnose its own sensors without a reference, onboard new ones through a declarative skill package with a capability-association graph, and keep working offline through a three-tier cloud-to-rule-engine fallback. We validate these capabilities, rather than field exposure tracking, in a 30-day fixed indoor deployment in Beijing of 1,896,789 records at 99.9% completeness. The agent decided on its own, without a reference, which channels to trust, identifying that the nominal ozone channel measures total oxidizing gas rather than ozone alone, a conclusion the manufacturer's datasheet independently confirms, while the PM2.5 and NO2 channels were separately corroborated as relatively usable against a nearby station (r = 0.90 and 0.86). Under a simulated cloud outage, it kept data collection uninterrupted by handing inference to the on-device local model. This is a single fixed indoor site and a design-and-functional validation; evaluation under mobile, rapidly changing microenvironments is future field work. Zhiwei shows that an environmental sensing device can manage its own data quality autonomously on-device, a prerequisite for trustworthy personal exposure monitoring.
Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we outline future research directions, including hybrid AI-biological models, AI-supported viral surveillance in healthcare environments, and the integration of explainable AI to enhance clinical trust.
Autism spectrum disorder (ASD) is a common, lifelong neurodevelopmental condition whose recorded prevalence, diagnostic delays, and uneven distribution of specialist services create a growing public health challenge. Conventional screening and diagnostic pathways depend heavily on scarce specialist expertise, contributing to long waiting times and unequal access across income settings, regions, sex, ethnicity, language, and social position. This narrative review synthesises current applications of artificial intelligence (AI) and machine learning in autism screening, diagnostic support, intervention, and longitudinal monitoring, and reframes the evidence through a public health and health equity lens. We argue that AI's most important contribution to autism care is unlikely to lie in marginal improvements in classification accuracy alone. Rather, its potential value lies in expanding access, supporting task-sharing, shortening diagnostic pathways, enabling population-oriented screening, and reaching under-recognised groups such as girls and women, adults, ethnic and linguistic minorities, and populations in low-resource settings. At the same time, AI may create an equity paradox: technologies intended to reduce disparities may reproduce or amplify them if they are trained on non-representative data, deployed across a digital divide, or governed without adequate attention to privacy, accountability, and community trust. Whether AI narrows or widens autism-related health inequalities will depend on choices about data diversity, low-resource design, co-design with autistic communities, equity-sensitive evaluation, clinical integration, and proportionate regulation.
Mental health challenges in Somalia are shaped by a complex interaction between fragile healthcare infrastructure and sociocultural factors influencing psychiatric care utilization. This commentary critically examines how stigma, spiritual and supernatural interpretations of mental illness, informal healing systems, limited mental health literacy, and severe shortages of psychiatric services shape help-seeking behaviors and access to formal mental healthcare in Somalia. In many Somali communities, mental illness may be interpreted through frameworks involving jinn possession, the evil eye, and black magic (sihr). Consequently, religious leaders and informal healers often serve as initial sources of help-seeking. However, reliance on informal systems of care also reflects broader structural barriers, including shortages of trained mental health professionals, inadequate integration of psychiatry into primary healthcare, financial constraints, inconsistent availability of psychotropic medications, and limited access to specialist services outside major urban centers. Stigma and limited public familiarity with psychiatric care may further contribute to delayed presentation and prolonged untreated illness. This commentary argues that cultural and structural barriers are deeply interconnected rather than separate explanations for poor psychiatric care utilization. Improving mental healthcare in Somalia therefore requires integrated and culturally responsive approaches that strengthen mental health systems while engaging trusted community and religious structures to improve accessibility, acceptability, and long-term sustainability of psychiatric care.
Research teams undertaking randomized clinical trials in low-and middle-income countries frequently encounter significant challenges, including limited infrastructure, cultural barriers to trust, and limited resources. Rheumatic heart disease (RHD) trials can be especially difficult because early stages of RHD often show no symptoms, there is low disease awareness among healthcare workers and the public, pediatric heart disease is often stigmatized, and disease management requires burdensome secondary antibiotic prophylaxis. To address these challenges, the RHD Research Collaborative in Uganda (RRCU) partnered with community members to establish a community-centered framework for trial design and implementation that incorporates group-based consent, frequent and personalized communication, and convenient, family-friendly participation structures. These components have been successfully adapted and applied across a series of RRCU studies, contributing to exceptionally high rates of consent, retention, and prophylaxis adherence. The RRCU's community-informed approach to trial design and implementation challenges assumptions about research feasibility in low-resource settings and demonstrates that high-quality, ethical pediatric research can be successfully conducted by engaging communities and integrating their values into all aspects of study design and execution.
Consumer Health Information (CHI) encompasses the dissemination of information and the cultivation of appropriate attitudes toward healthcare, as well as specific professional skills, aimed at altering behaviors and enhancing the health status of consumers. The aim of this study was to introduce the concept, features, and associated issues of CHI, provide an overview of current research, and examine the information-seeking behavior of health information consumers. On February 28, 2025, the keywords "consumer health information," "health literacy," and their related terms were searched in PubMed, Web of Science, Google Scholar, and grey literature sources. Out of 1,058 retrieved records, 55 met the inclusion criteria and were analyzed to extract the relevant characteristics. The findings of this study indicate that consumer health information (CHI) is a dynamic concept that encompasses the provision of information and the enhancement of skills related to healthcare, with the aim of influencing behavior and improving consumers' health. A review of the existing literature demonstrates that the evaluation components of this concept encompass a range that includes comprehensiveness, credibility, readability, and usefulness. Furthermore, the Internet, social media, and artificial intelligence tools serve as the primary platforms for searching for and accessing such information. The results also suggest that challenges such as misinformation, information overload, and limitations in health literacy significantly influence information-seeking behavior, underscoring the necessity for increased attention to the role of librarians as trusted information intermediaries. The necessity of disseminating accurate and reliable health information tailored to consumers' needs is underscored by the increasing trend of scientific output in this domain in recent years. It is essential to develop standard guidelines and regulations, as well as to create appropriate infrastructure for CHI at the macro level. Furthermore, conducting economic studies to evaluate the return on investment and utility of CHI can aid policymakers in their decision-making processes.
Fragmented primary health care in China fails to tackle the growing burden of noncommunicable diseases. Despite substantial investment, fewer than half of patients with diabetes achieve glycaemic control. Tianjin's 2020-2023 reform established a public-private partnership model where WeDoctor managed community health centres under a capitation scheme. This strategy integrated: (i) monthly prepaid capitation covering all diabetes-related outpatient services; (ii) claims auditing and clinical decision support using artificial intelligence (AI); (iii) dedicated health managers for care coordination; and (iv) redesigned services incorporating complication screening and digital medication management. A pilot study including 494 945 patients with diabetes compared three care models from 2022 to 2023: WeDoctor-community health centre care, hospital care and usual care. Tianjin city serves 15 million residents through 177 hospitals and 266 community health centres. Chronic disease management is fragmented: the Health Commission regulates care standards, while the Insurance Bureau controls funding. Visits for diabetes at WeDoctor health centres increased 2.6% (0.7/26.6) but declined 10.6% (-3.8/35.7) for hospital-based care and 2.3% (-0.8/34.1) for usual care. All groups reduced outpatient expenditure. The WeDoctor model generated a 37.62 million United States dollars (US$) surplus, boosted health centre diabetes revenue by 65% (US$ 154 577/237 805) and raised physician annual salaries by 30% (US$ 5172/17 241). More than three quarters of patients expressed satisfaction with and trust in the WeDoctor model. Integrating capitation financing with third-party governance and AI support can strengthen primary health care, contain costs and enhance patient-centred care. La fragmentation des soins de santé primaires en Chine ne permet pas de faire face au fardeau croissant des maladies non transmissibles. Malgré des investissements considérables, moins de la moitié des patients diabétiques parviennent à contrôler leur glycémie. La réforme de Tianjin pour la période 2020–2023 a mis en place un modèle de partenariat public-privé dans le cadre duquel WeDoctor gérait les centres de santé communautaires selon un système de forfait par habitant. Cette stratégie intégrait: (i) un forfait mensuel prépayé couvrant tous les services de consultation externe liés au diabète; (ii) un audit des demandes de remboursement et un soutien aux décisions cliniques à l’aide de l’intelligence artificielle (IA); (iii) des cadres de santé dédiés à la coordination des soins; et (iv) des services repensés pour intégrer le dépistage des complications et la gestion numérique des médicaments. Une étude pilote portant sur 494 945 patients diabétiques a comparé trois modèles de soins entre 2022 et 2023: les soins dispensés par WeDoctor et les centres de santé communautaires, les soins hospitaliers et les soins habituels. La ville de Tianjin dessert 15 millions d’habitants grâce à 177 hôpitaux et 266 centres de santé communautaires. La prise en charge des maladies chroniques est fragmentée: la Commission de la santé réglemente les normes de soins, tandis que le Bureau des assurances oriente le financement. Les consultations pour le diabète dans les centres de santé WeDoctor ont augmenté de 2,6% (0,7/26,6), mais ont diminué de 10,6% (−3,8/35,7) pour les soins hospitaliers et de 2,3% (−0,8/34,1) pour les soins habituels. Tous les groupes ont réduit leurs dépenses pour des soins ambulatoires. Le modèle WeDoctor a généré un excédent de 37,62 millions de dollars américains (USD), augmenté les recettes des centres de santé liées au diabète de 65% (154 577 / 237 805 USD) et accru de 30% les salaires annuels des médecins (5 172 / 17 241 USD). Plus des trois quarts des patients ont exprimé leur satisfaction et leur confiance dans le modèle WeDoctor. L’intégration du financement par forfait à une gouvernance par des tiers et à un soutien par l’IA permet de renforcer les soins de santé primaires, de maîtriser les coûts et d’améliorer les soins axés sur le patient. La fragmentación de la atención primaria de salud en China no logra hacer frente a la creciente carga de las enfermedades no transmisibles. A pesar de las importantes inversiones realizadas, menos de la mitad de los pacientes con diabetes logran un control glucémico adecuado. La reforma aplicada en Tianjin entre 2020 y 2023 estableció un modelo de asociación público-privada en el que WeDoctor gestionaba centros comunitarios de salud mediante un sistema de capitación. Esta estrategia integró: (i) una capitación mensual prepagada que cubría todos los servicios ambulatorios relacionados con la diabetes; (ii) la auditoría de reclamaciones y el apoyo a la toma de decisiones clínicas mediante inteligencia artificial (IA); (iii) gestores sanitarios dedicados a la coordinación asistencial; y (iv) servicios rediseñados que incorporaban el cribado de complicaciones y la gestión digital de la medicación. Un estudio piloto que incluyó a 494 945 pacientes con diabetes comparó tres modelos asistenciales entre 2022 y 2023: la atención prestada por WeDoctor en centros comunitarios de salud, la atención hospitalaria y la atención habitual. La ciudad de Tianjin presta servicios a 15 millones de habitantes a través de 177 hospitales y 266 centros comunitarios de salud. La gestión de las enfermedades crónicas está fragmentada: la Comisión de Salud regula los estándares asistenciales, mientras que la Oficina de Seguros controla la financiación. Las consultas por diabetes en los centros de salud de WeDoctor aumentaron un 2,6% (0,7/26,6), mientras que disminuyeron un 10,6% (-3,8/35,7) en la atención hospitalaria y un 2,3% (-0,8/34,1) en la atención habitual. Todos los grupos redujeron el gasto ambulatorio. El modelo WeDoctor generó un superávit de US$ 37,62 millones, aumentó en un 65% los ingresos por atención de la diabetes en los centros de salud (US$ 154 577/237 805) e incrementó en un 30% los salarios anuales de los médicos (US$ 5172/17 241). Más de tres cuartas partes de los pacientes expresaron satisfacción y confianza en el modelo WeDoctor. La integración de la financiación mediante capitación con la gobernanza por terceros y el apoyo de la IA puede fortalecer la atención primaria de salud, contener los costes y mejorar la atención centrada en el paciente. تعجز الرعاية الصحية الأولية المجزأة في الصين عن مواجهة العبء المتزايد الناتج عن الأمراض غير المعدية. والرغم من الاستثمارات الضخمة، فإن أقل من نصف مرضى السكري يحققون السيطرة على مستوى السكر في الدم. أدت الإصلاحات في مدينة تيانجين خلال الفترة من 2020 إلى 2023 لتأسيس نموذج للشراكة بين القطاعين العام والخاص، حيث تولت شركة WeDoctor إدارة المراكز الصحية المجتمعية ضمن نظام الدفع الفردي. وقد اشتملت هذه الاستراتيجية على ما يلي: (1) دفع شهري مسبق يغطي جميع خدمات العيادات الخارجية المتعلقة بمرض السكري؛ و(2) تدقيق المطالبات ودعم القرارات الإكلينيكية باستخدام الذكاء الاصطناعي؛ و(3) مديرين للرعاية الصحية متخصصين لتنسيق الرعاية؛ و(4) خدمات أُعيد تنسيقها لتشمل فحص المضاعفات والإدارة الرقمية للأدوية. وقد قارنت دراسة تجريبية، شملت 494945 مريضًا بمرض السكري، بين ثلاثة نماذج للرعاية خلال الفترة من 2022 إلى 2023: الرعاية المقدمة في مراكز WeDoctor الصحية المجتمعية، والرعاية في المستشفيات، والرعاية المعتادة. تخدم مدينة تيانجين 15 مليون نسمة من خلال 177 مستشفى و266 مركزًا صحيًا مجتمعيًا. يتسم نظام إدارة الأمراض المزمنة بأنها مجزأة، حيث تتولى لجنة الصحة تنظيم معايير الرعاية، بينما يتحكم مكتب التأمين في التمويل. ارتفعت زيارات مرضى السكري إلى مراكز WeDoctor الصحية بنسبة %2.6 (0.7/26.6)، بينما انخفضت بنسبة %10.6 (3.8-/35.7) للرعاية المقدمة في المستشفيات، وبنسبة %2.3 (0.8-/34.1) للرعاية المعتادة. وقد خفضت جميع المجموعات نفقات العيادات الخارجية. وحقق نموذج WeDoctor فائضًا قدره 37.62 مليون دولار أمريكي، ورفع عائد خدمات مرض السكري في مراكز الرعاية الصحية بنسبة %65 (154577/237805 دولارًا أمريكيًا)، ورفع الرواتب السنوية للأطباء بنسبة %30 (5172/17241 دولارًا أمريكيًا). وأعرب أكثر من ثلاثة أرباع المرضى عن رضاهم وثقتهم بنموذج WeDoctor. إن دمج تمويل الدفع الفردي مع حوكمة الجهة الخارجية، ودعم الذكاء الاصطناعي، يمكن أن يعزز الرعاية الصحية الأولية، ويحد من التكاليف، ويعزز الرعاية التي تركز على المريض. 中国基层医疗卫生比较分散，难以应对非传染性疾病日益严峻的负担。尽管投入较大，仍仅有不到半数的糖尿病患者实现血糖达标。. 天津市 2020-2023 年基层医改推行公私协同模式，由微医采用“按人头付费”制度协助管理社区卫生服务中心。该举措涵盖以下四点：(1) 按月预付的人头付费，覆盖与糖尿病相关的所有门诊服务；(2) 采用人工智能来开展医保费用审核和临床决策支持；(3) 配备专职的健康管理人员负责照护协调；以及 (4) 重构服务内容，新增并发症筛查并纳入数字化用药管理。一项于 2022 年至 2023 年开展的纳入 494945 名糖尿病患者的试点研究，对比了三种诊疗模式：微医联合社区卫生服务中心模式、医院诊疗模式及常规诊疗模式。. 天津市现有 177 家医院和 266 家社区卫生服务中心，服务 1500 万居民。慢性病管理体系分散：卫生健康部门监管诊疗服务标准，医保部门负责管控经费。. 微医联合卫生服务中心模式下的糖尿病就诊人次增长 2.6% (0.7/26.6)；而医院诊疗模式下的就诊人次下降 10.6% (−3.8/35.7)，常规诊疗模式下的就诊人次下降 2.3% (−0.8/34.1)。所有模式下的门诊费用均有所减少。微医模式产生 3762 万美元的结余，卫生服务中心的糖尿病相关收入提升 65%（154577/237805 美元），医生年薪提高 30%（5172/17241 美元）。超过四分之三的患者对微医模式表示满意和信任。. 按人头付费模式与第三方治理及人工智能技术相结合，能够夯实基层医疗卫生服务能力、控制医疗成本，同时提升以患者为中心的服务质量。. Фрагментированная структура первичного медико-санитарного обслуживания в Китае не справляется с растущим бременем неинфекционных заболеваний. Несмотря на существенные инвестиции, гликемического контроля удается достичь менее чем для половины пациентов с диабетом. В рамках реформы, проведенной в Тяньцзине в 2020–2023 годах, была создана модель государственно-частного партнерства, где компания WeDoctor управляла муниципальными центрами здравоохранения по схеме подушевого финансирования. Стратегия включала: (i) ежемесячное предварительное подушевое финансирование, которое покрывало все амбулаторные услуги, связанные с диабетом; (ii) аудит страховых требований и помощь в принятии клинических решений с использованием искусственного интеллекта (ИИ); (iii) назначение специальных координаторов медицинского обслуживания; (iv) изменения в структуре обслуживания, в том числе скрининг осложнений и цифровое управление медикаментозной терапией. В пилотном исследовании с участием 494 945 пациентов с диабетом сравнивались три модели лечения в период с 2022 по 2023 год: помощь на базе муниципальных центров здравоохранения WeDoctor, лечение в больнице и обычное медицинское обслуживание. В городе Тяньцзинь на 15 миллионов жителей приходится 177 больниц и 266 муниципальных центров здравоохранения. Ведение пациентов с хроническими заболеваниями осуществляется фрагментированно: Комиссия по здравоохранению регулирует стандарты оказания помощи, а Бюро страхования контролирует финансирование. Частота посещения медицинских центров компании WeDoctor по поводу диабета возросла на 2,6% (+0,7 при исходных 26,6), однако снизилась на 10,6% (–3,8 при исходных 35,7) для лечения на базе стационара и на 2,3% (–0,8 при исходных 34,1) для обычного медицинского обслуживания. Во всех группах сократились издержки на амбулаторное лечение. Модель WeDoctor обеспечила профицит в размере 37,62 млн долл. США, увеличив доходы медицинских центров от оказания помощи пациентам с диабетом на 65% (+154 577 долл. США при исходных 237 805), а также повысив ежегодную зарплату врачей на 30% (+5172 долл. США при исходных 17 241). Более трех четвертей пациентов выразили удовлетворенность моделью WeDoctor и доверие к ней. Сочетание подушевого финансирования, модели управления силами сторонних организаций и использования искусственного интеллекта может укрепить систему первичной медико-санитарной помощи, оптимизировать издержки и повысить ориентированность медицинской помощи на потребности пациентов.
Diabetic retinopathy (DR) is a leading cause of preventable blindness, with the highest burden falling on populations living in urban slums and peri-urban settlements in low- and middle-income countries. These communities often remain outside the reach of conventional health systems due to systemic neglect, migratory lifestyles, and fragile infrastructure. Traditional facility-based screening models are often inaccessible, underscoring the urgent need for decentralized, community-embedded solutions. This review synthesizes global evidence on community and allied health worker (CAHW)-led DR screening initiatives in urban slum contexts, drawing from 22 studies conducted across Asia, Africa, Latin America, and Oceania. In addition, field experience from a DR screening program in Dharavi, Mumbai, one of Asia's largest informal settlements, is included to illustrate operational realities. Findings highlight the transformative potential of task-shifting DR screening responsibilities to CAHWs, many of whom are recruited from the communities they serve. These workers effectively carried out tasks including health education, visual acuity screening, portable fundus photography, referral navigation, and digital data management. With structured training and supervision, CAHWs were found capable of taking on responsibilities conventionally reserved for ophthalmic professionals. Their cultural familiarity and embedded presence also enabled them to overcome key barriers such as distrust, mobility, and language, improving screening uptake and follow-through in underserved populations. Despite clear benefits, challenges persist. Programs often lack standardized competency frameworks, adequate incentives, digital tools, and integrated referral systems. Addressing these gaps is essential for sustaining impact.
Off-grid direction-of-arrival (DOA) estimation based on sparse Bayesian learning (SBL) can alleviate angular discretization mismatch, but its practical performance may be affected by unreliable posterior relevance statistics, sensitivity of effective error precision learning, and unstable offset correction. This paper proposes a reliability-guided stabilized off-grid SBL method for multisnapshot DOA estimation. The method is developed within the standard first-order multiple-measurement-vector Bayesian model and introduces three stabilization modules. First, a confidence-guided MAP-type shrinkage relevance update is introduced to suppress weak and non-dominant posterior components through reliability-controlled non-expansive shrinkage. Second, a posterior-concentration-guided damped noise update is introduced to stabilize scalar effective error precision learning when the sparse support is uncertain. Third, a trust-region cubic-regularized Newton refinement is formulated to obtain bounded active-support off-grid corrections from the posterior expected reconstruction error. Simulation results under off-grid deviation, varying SNRs, varying snapshot numbers, different source separations, and random-angle scenarios show that the proposed method achieves competitive and stable estimation performance compared with representative classical and sparse Bayesian baselines.
Vaccine hesitancy contributes to the dampened public enthusiasm for vaccination in several countries, including Nigeria. Human papillomavirus (HPV) vaccine acceptance is influenced by various factors; studies have examined some of these factors in sub-Saharan Africa, including Nigeria. This study aimed to examine HPV vaccine hesitancy and associated factors among parents of adolescent girls in Ibadan North East Local Government Area based on the WHO 3C model. A cross-sectional mixed-methods study was conducted, comprising four Focus Group Discussion sessions with parents and semistructured, interviewer-administered questionnaires, using a multistage sampling procedure to select 402 parents. Quantitative data were analysed using descriptive statistics, χ2 tests and logistic regression analysis with IBM SPSS V.22. Qualitative data were thematically analysed using ATLAS.ti V.25. The mean age of parents was 43.7±7.4 years, 66.9% were women, 50.7% identified as Christians and 95.0% belonged to the Yoruba ethnic group. Respondents were knowledgeable about HPV infection (51.5%) and HPV vaccine (64.7%); 61.4% had a positive attitude towards HPV vaccination. About 27.4% were hesitant to receive the HPV vaccine. The perceived necessity of vaccine (adjusted OR (AOR)=4.61, CI 1.14 to 18.72), trust in the efficacy of vaccines (OR=3.54, CI 1.09 to 11.51) and HPV vaccine safety (AOR=8.46, CI 2.73 to 26.18) had a higher influence on HPV vaccine hesitancy, while availability of the vaccine (AOR=0.23, CI 0.11 to 0.50) had a lesser influence. There was a significant level of HPV vaccine hesitancy among parents of adolescent girls, and associating factors included limited knowledge about HPV and its vaccine, as well as concerns about vaccine safety, vaccine necessity and vaccine availability. There is a need for targeted public health interventions that contribute to the prevention of HPV-related diseases.